Shared and Disease-specific Genetic Study among IgA Nephropathy, Henoch-schonlein Purpura Nephritis and Lupus Nephritis

JI Program: Renal


IgA nephropathy (IgAN), lupus nephritis (LN), and Henoch-Schonlein purpura nephritis (HSPN) are three types of glomerulonephritis (GN) with high prevalence in both Chinese and American populations, leading to end-stage renal disease (ESRD), which means the requirement of organ replacement therapy, itself associated with high cost and unacceptable levels of mortality in both countries. Epidemiology studies show a significant contribution of ethnicity in the prevalence and severity of IgAN and LN diseases (e.g., African-American and Chinese are “sicker” than Europeans). The Michigan Medicine and PKUHSC renal divisions have joined their efforts recently in another collaborative study to develop non-invasive molecular markers for GN patients. This new unique opportunity to collaborate between our two countries and combine our complementary resources and expertise in both systems genetics and system biology will allow us to: 1) build and validate a fine mapping of the genetic variant regions associated with IgAN, LN, and HSPN; and 2) integrate the characterized putative functional variants into their disease functional context. Our pilot study will generate essential information towards the goal of customizing effective patient care with disease- and patient-specific therapies that are less toxic, thus minimally affecting the central immune mechanisms and reducing the treatment costs in both China and the U.S. 


  • Completed imputation and statistical analysis of genome wide association studies (GWAS) from controls, IgAN, LN, and IgA vasculitis patients.  Our study is one of the largest IgAN GWAS up to date, and in the same center, avoiding diagnosis (renal biopsy) heterogeneity.
  • Our study reveals additional loci and novel genes for genetic predisposition to IgAN, which may shed novel biological mechanism.
  • Machine learning methods are being developed to support prediction analyses using GWAS data.